Digital images can be formed by many devices and used for many practical purposes. Devices include cameras with image sensors operating on visible or infrared light, such as a charge-coupled device (CCD) image sensor or a complementary metal-oxide-semiconductor (CMOS) image sensor, line-scan sensors, flying spot scanners, electron microscopes, X-ray devices including computed tomography (CT) scanners, magnetic resonance imagers, and other devices known to those skilled in the art. Practical applications are found in industrial automation, medical diagnosis, satellite imaging for a variety of military, civilian, and scientific purposes, photographic processing, surveillance and traffic monitoring, document processing, and many other applications.
To serve these applications, the images formed by the various devices can be analyzed by machine vision systems to extract appropriate information. One form of analysis that is of considerable practical importance is determining the position, orientation, and size of patterns in an image that correspond to objects in the field of view of the imaging device. Pattern detection methods are of particular importance in industrial automation, where they are used to guide robots and other automation equipment in semiconductor manufacturing, electronics assembly, pharmaceuticals, food processing, consumer goods manufacturing, and many others.
Pattern detection methods can operate on images that have been subject to perspective distortions. However, the perspective distortions tend to increase the computational complexity and the computing time of pattern detection methods. Therefore, as a preprocessing step, images can be rectified to remove perspective distortions prior to performing the pattern detection methods.